Methods and systems for optimizing engine selection using machine learning modeling

Abstract

A system for optimizing selection of transcription engines using a combination of selected machine learning models. The system includes a plurality of preprocessors that generate a plurality of features from a media data set. The system further includes a deep learning neural network model, a gradient boosted machine model and a random forest model used in generating a ranked list of transcription engines. A transcription engine is selected from the ranked list of transcription engines to generate a transcript for the media dataset.

Description

CROSS-REFERENCE TO RELATED APPLICATION[0001]This application claims priority to U.S. Provisional Application No. 62 / 638,745, filed Mar. 5, 2018, U.S. Provisional Application No. 62 / 633,023, filed Feb. 20, 2018, and U.S. Provisional Application No. 62 / 540,508, filed Aug. 2, 2017, each of which are hereby incorporated in their entirety by reference.TECHNICAL FIELD[0002]The claimed invention relates to optimizing engine selection, and in some aspects to methods and systems for optimizing the selection of transcription and / or object recognition engines using machine learning modeling.BACKGROUND[0003]Since the advent of the Internet and the video-recording-enabled smartphone, a massive amount of multimedia is being generated every day. For example, because people can record live events with ease and simplicity, new multimedia (e.g., music and / or videos) are constantly being generated. There is also ephemeral media, such as radio broadcasts. Once these media are created, there is no exist...

AI Technical Summary

Benefits of technology

The patent describes a system that uses machine learning models to optimize the selection of transcription engines. The system generates features from media data and includes a deep learning neural network model, a gradient boosted machine model, and a random forest model to improve the detection of patterns and the generation of classified categories. The system then ranks a list of transcription engines based on the learning from the machine learning models. The chosen transcription engine then ingests the features and generates a transcript for the selected media data. The system also includes preprocessors for generating features and a multi-model stacking model for combining the results from the three machine learning models. Additionally, the system includes modules to reduce bias and variance in the model predictions. The database used in the system is a temporal elastic database. The technical effect of the patent is to improve the efficiency and accuracy of transcription engines for various applications such as speech-to-speech translation, speech-to-speech and audio-to-whatever translation.

Problems solved by technology

Once these media are created, there is no existing technology to optimally transcribe all of the content therein.

It is estimated that about 80% of the world data is unreadable by machines.

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